Known in the art are computer cluster arrangements comprising computing nodes including at least one processor as well as accelerators being tightly coupled to the computing nodes for outsourcing computations of high resource requirements. A tight coupling of accelerators to computing nodes results in a static assignment and leads to over- or under-subscription of accelerators. This may lead to a lack of resources or may lead to an excessive supply of resources. Such a static assignment of accelerators to computing nodes does furthermore not provide fault tolerance in case of accelerator failures.
The publication “rCUDA: reducing the number of GPU-based accelerators in high performance clusters” by Jose Duato, Rafael Mayo et al., International Conference on High Performance Computing and Simulation (HPCS), Issue Date: Jun. 28, 2010-Jul. 2, 2010, on pages 224-231, describes a frame work that enables remote GPU acceleration in high performance clusters, thus allowing a reduction in the number of accelerators installed on the cluster. This may lead to energy, acquisition, maintenance and space savings.
The publication “A package for open CL based heterogeneous computing on clusters with many GPU devices” by Amnon Barak, et al. of the Department of Computer Science from Hebrew University of Jerusalem describes a package for running OpenMP, C++ an unmodified OpenCL applications on clusters with many GPU devices. Furthermore, an implementation of the OpenCL specifications and extensions of the OpenMP API that allow applications on one hosting-node to transparently utilize cluster-wide devices is provided. FIG. 1 shows a computer cluster arrangement according to the state of the art. The computer cluster arrangement comprises several computations nodes CN, which are interconnected and jointly compute a computation task. Each computation node CN is tightly coupled with an accelerator Acc. As can be seen in FIG. 1 a computation node CN comprises an accelerator unit ACC which is virtually integrated on the computation node CN along with a microprocessor, for instance a central processing unit CPU. As introduced above, the fixed coupling of accelerators Acc to computation nodes CN leads to an over- or under subscription of accelerators Acc depending on the computation task. Furthermore, no fault tolerance is provided in case of failure of one of the accelerators Acc. In the known computer cluster arrangement according to FIG. 1 computing nodes CN communicate with each other over an infrastructure, wherein accelerators Acc do not exchange information directly, but require a computation node CN interfacing the infrastructure IN for data exchange.